This SBIR Phase I project seeks to produce a first of its kind educational technology application for the development of emotional literacy among adolescent and adult learners. Emotional literacy is increasingly recognized as a universal need that is critical to one's academic and professional success and psychological health, as well as for society's cohesion and tolerance. At the same time, modern emotion science indicates that emotions operate on the basis of certain basic, universal principles, which represent the deep structure of our social and emotional life. The present application will focus on teaching these fundamental principles, encapsulated by a comprehensive emotion system model developed based on the latest scientific research. To foster deep learning, the training will emphasize inference-making and the use of simulations, while being grounded in high quality narrative such as literature and film. Training exercise generation will be partially automated to provide content variability. The project has the potential to have a broader impact on education practice and outcomes by leading to more robust, transferable, and measurable learning of social-emotional concepts and skills. It promises to make a further valuable societal contribution by offering an affordable, effective and engaging mobile solution for enhancing emotional literacy among the general population, helping to build a more aware, tolerant, just, and peaceful society. At the same time, the application has the potential to capture a significant portion of a combined $1.5 billion social-emotional learning and consumer emotional wellness market, resulting in a self-sustaining business generating jobs and significant income for tax revenue. The project entails a number of technical innovations, including: a data model and management system for the encoding of emotion episodes in literature and film; an emotion exercise generation engine for automating the creation of training exercises; an effective user interface for the presentation of emotion principles and the associated training exercises, particularly those involving emotion system simulation; and emotion knowledge performance analytics dashboard(s). The main goals of the proposed R&D include: assessing the viability of developing the emotion exercise generation engine and a production-ready version of the application; evaluating the benefits of the training for application users; and assessing the commercial viability of the product in the educational and consumer markets. The technical goals will be achieved through rapid, agile development using technologies such as python, a cross-platform mobile development framework such as React Native, and a graph database like Neo4J. Platform training benefits will be evaluated using randomized control studies at multiple stages of product development to allow for iteration and improvement. Commercial viability will be assessed through direct outreach to schools and other educational institutions, as well as a beta launch aimed at the consumer market. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
